tensorflow: [Windows] Tensorflow GPU fails to find CUDA.

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For bugs or installation issues, please provide the following information. The more information you provide, the more easily we will be able to offer help and advice.

What related GitHub issues or StackOverflow threads have you found by searching the web for your problem?

NONE (Tensorflow for Windows is very new).

Environment info

Operating System: Windows 8.1 (Conda 4.2.9)

conda --version conda 4.2.9

Installed version of CUDA and cuDNN: (please attach the output of ls -l /path/to/cuda/lib/libcud*): nvcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2016 NVIDIA Corporation Built on Sat_Sep__3_19:05:48_CDT_2016 Cuda compilation tools, release 8.0, V8.0.44

If installed from binary pip package, provide:

1. A link to the pip package you installed: pip install --upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-0.12.0rc0-cp35-cp35m-win_amd64.whl

2. The output from python -c "import tensorflow; print(tensorflow.__version__)".

python -c "import tensorflow; print(tensorflow.__version__)" 0.12.0-rc0 I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_80.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:119] Couldn't open CUDA library cudnn64_5.dll I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\cuda\cuda_dnn.cc:3459] Unable to load cuDNN DSO I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll locally I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stream_executor\dso_loader.cc:128] successfully opened CUDA library curand64_80.dll locally

If possible, provide a minimal reproducible example (We usually don’t have time to read hundreds of lines of your code)

What I did: I attempt to run some tensorflow code that works within ubuntu CPU instance.

Code: MNIST simple autoencoder (requires MNIST dataset); CODE

Error Message error_message:

In [10]: %run CNN_autoencoder.py
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stre
am_executor\dso_loader.cc:128] successfully opened CUDA library cublas64_80.dll
locally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stre
am_executor\dso_loader.cc:119] Couldn't open CUDA library cudnn64_5.dll
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stre
am_executor\cuda\cuda_dnn.cc:3459] Unable to load cuDNN DSO
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stre
am_executor\dso_loader.cc:128] successfully opened CUDA library cufft64_80.dll l
ocally
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stre
am_executor\dso_loader.cc:128] successfully opened CUDA library nvcuda.dll local
ly
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stre
am_executor\dso_loader.cc:128] successfully opened CUDA library curand64_80.dll
locally
Extracting MNIST_data/train-images-idx3-ubyte.gz
Extracting MNIST_data/train-labels-idx1-ubyte.gz
Extracting MNIST_data/t10k-images-idx3-ubyte.gz
Extracting MNIST_data/t10k-labels-idx1-ubyte.gz
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core
\common_runtime\gpu\gpu_device.cc:885] Found device 0 with properties:
name: GeForce GTX 965M
major: 5 minor: 2 memoryClockRate (GHz) 0.9495
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.86GiB
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core
\common_runtime\gpu\gpu_device.cc:906] DMA: 0
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core
\common_runtime\gpu\gpu_device.cc:916] 0:   Y
I c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core
\common_runtime\gpu\gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (d
evice: 0, name: GeForce GTX 965M, pci bus id: 0000:01:00.0)
E c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\core
\common_runtime\gpu\gpu_device.cc:586] Could not identify NUMA node of /job:loca
lhost/replica:0/task:0/gpu:0, defaulting to 0.  Your kernel may not have been bu
ilt with NUMA support.
WARNING:tensorflow:From C:\Users\Gregoty\Programming\cs231n\repo\project\tensorf
low\autoencoder\CNN_autoencoder.py:188 in <module>.: initialize_all_variables (f
rom tensorflow.python.ops.variables) is deprecated and will be removed after 201
7-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
number of test       =  10000
number of train      =  55000
number_of validation =  5000
Done splitting up test data set;
Starting training loop.
Epoch:  0
Shuffling the training data;
F c:\tf_jenkins\home\workspace\release-win\device\gpu\os\windows\tensorflow\stre
am_executor\cuda\cuda_dnn.cc:221] Check failed: s.ok() could not find cudnnCreat
e in cudnn DSO; dlerror: cudnnCreate not found

What other attempted solutions have you tried?

Tried running it through ipython (conda), python (initiated by msys2)

Logs or other output that would be helpful

(If logs are large, please upload as attachment or provide link).